Common Kafka Connect issues span connector misconfiguration, resource constraints, and operational challenges that show up as failed data transfer, lag, or inconsistent data in the target system.
Key Points: • Misconfigured connector properties (wrong connection strings, credentials, or serialization settings) prevent connectors from starting or cause immediate task failures. • Under-provisioned workers (insufficient CPU, memory, or tasks.max) lead to slow throughput and growing lag under high data volume. • Connectors that crash or lose their offset tracking can produce inconsistent or duplicated data downstream. • Network issues between Connect and the source/sink system can interrupt data flow and cause repeated retries or task failures. • Upgrading connector plugin versions can introduce compatibility issues, requiring careful version pinning and testing before rollout.
Example: A JDBC sink connector might silently fall behind because tasks.max is left at its default of 1, forcing all writes through a single task even though the target database and topic could easily support several parallel tasks.
Interview Tip: A concise interview answer is:
"Most Kafka Connect issues I've hit trace back to either connector configuration mistakes, under-provisioned workers or tasks.max being too low for the workload, or network flakiness to the external system — so I always check connector logs and worker resource usage first before assuming it's a Kafka-side problem."